Semantic searches in a business intelligence system

ABSTRACT

A computer-implemented method of executing a user query includes presenting a user interface to allow a user to enter a query, receiving a user-entered textual request through the interface, launching a search service to rewrite the textual request into a search query, sending the search query to a presentation server, receiving an answer to the query, and returning the answer to the user as a graphical representation. A computer-implemented method includes receiving a crawl request from a user, launching a crawl manager to monitor the crawl request and track statistics related to the crawl, starting a crawl task based upon the crawl request, indexing a business intelligence presentation server to create a data index, and storing the data index.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. Provisional Application No. 62/055,402, filed Sep. 25, 2014, which is incorporated herein in its entirety.

BACKGROUND

Business intelligence systems typically use large, very complex data stores in the form of databases and data warehouses, as examples. These stores are accessible by sophisticated trained users that can produce the requested information by using complex query structures and reports in the vernacular of the databases.

Typical users do not have this level of sophistication and training to access this information. These users area comfortable with ‘textual searching’ such as that used in Google® and other search engines. Current systems do not have the capability to provide the user with this type of access, nor do they produce the responses in a format that the user can understand.

Further, with the advent of mobile devices, such as smart phones and tablets, user have become accustomed to accessing data and getting answers wherever they are located. They want to input textual questions, even into complex, large, databases and data warehouses, and receive the answers in an easily understandable format.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an overall system architecture for a business intelligence search program.

FIG. 2 shows a diagram of a search system usable in a business intelligence program.

FIGS. 3-10 show examples of user interfaces for textual searching.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 shows an overall system diagram for a business intelligence search engine 10. The user device, which may be referred to as a client, 12, upon which is running a search query 14. In one embodiment, the query search service is a Java® service running in a J2EE environment. The search is provided to a business intelligence presentation service, also referred to as the presentation layer, 16 and has a scheduler 17 that may schedule and manage the data indexing. The data indexing may take the form of data crawls through the data structure and multiple crawls may be occurring at once. The business intelligence (BI) server 18 then accesses the various data stores available, which may include databases, data warehouses, etc.

The search system allows a user that does not have a background in databases and data warehouses to perform a textual search of the data to get an answer to a simple query. The answer may take the form of a graphical representation of the answer, allowing the user to visualize the answer.

The search system has several components that operate to take the user's request and produce an answer in a form the user can quickly understand. Through a user interface, the user can enter a query into the business intelligence system (BI) in the form of a ‘regular’ question. Examples include queries like, “What is the revenue of iPods for the North East in 2011?” or “Compare 2011 Q1 revenue to 2012 Q3.”

The search service 30 launches a search query rewriter 32 to generate a query in terms that the data index storage repository 29 can ‘understand.’ These are then queried by the index searcher 34. For example, the questions above may be rewritten to queries in a query language such as SQL (Structured Query Language) corresponding to “Revenue iPods North East 2011” and “Revenue 2011 Q1 vs Revenue 2012 Q3.” These are then used by the index searcher 34 to access the disk 20. A text to query generator 36 takes the indexed searches and generates a query for which the visualization, such as a bar chart or other graph, is generated. The queries and their visualizations are then sent to the presentation server 38 (BIPS) and the BI server to generate SQL Queries 40.

The Crawl Service may launch according to a schedule. As mentioned before, the Crawl Service and the Query service may be Java® services running in a J2EE environment. The crawl service 22 invokes a crawl manager 24 that manages the various crawls that may be running at the same time. The crawl manager may, for example, limit the number of crawls operating simultaneously to one, and set up a queue for the other crawl requests.

The crawl manager 22 launches a crawl task 24. The crawl task may be initiated through the BI Search Semantic Indexing Scheduler, an automated process that performs the crawl. The progress of a crawl, such as the statistics of number of areas indexed, number of subject area columns indexed, total number data values indexed, etc., may be stored in the business intelligence log files as informational entries. The areas indexed may include metadata, such as measures, dimensions and data members and attributes. Once a crawl has been completed, the admin user may be notified using the delivery of a message, success or failure. Further, the system may provide monitoring of the crawl. The crawling user may receive an email indicating that the crawl has begun, with the option of a link to allow the user to stop the crawl. When the crawl completes, another email may notify the user of its completion.

The crawl task in turn then launches query executors 26 that convert the queries into SQL, or other language, queries that will execute on the data gathered by the crawl task. In order to make use of that data later, the query executors start index writers 28 to index the queries and write them to some sort of storage repository 29. The query executors also send the queries to the presentation server, which in turn sends the queries to the business intelligence server. The indexes of the queries will be where the user's search will operation

When the user enters the desired search, the search process converts it to queries and accesses the index of the semantic layer (rpd file) to provide the answers. As discussed above, the search system allows the user to input ‘textual searches’ in the form most users are familiar and returns a graph that allows a user to understand the answer easily. When the users launches the search interface on the user's device, typically a smart phone or other mobile device, that starts the search service 30. The user then inputs a search term or terms, which can be of varying levels of complexity.

FIGS. 3-10 show examples of these interfaces and help in explaining some assumptions that are made by the system to provide the user with information and options. For example in FIG. 3, the user has selected one search term, ‘revenue.’ Referring back to FIG. 2, the search service receives the user input for revenue and passes it to the search query rewriter 32 then takes the term and rewrites it into a query language form, such as SQL. The rewritten query is then used by the index searchers to locate the revenue information located in the data repository and retrieves that information for the text to query generator 36. The text to query generator converts the results of the search into some sort of user-friendly format, referred to here as a visualization, to be presented by the presentation server 30. The search and its resultant metadata, which may include the SQL statements, the results from the index searchers, and the visualization, may then be stored in the BI server 40.

As the user navigates the search and results, the system gathers statistics to find the most relevant content for a given query. This information will be used to make the next execution of that query work faster and produce more relevant results. Similarly, the system ‘learns’ common analysis and visualization patterns. This will become clearer as the discussion goes through the user interfaces.

In FIG. 3, the user wants to know the total revenue, so just types in “Revenue” at the search bar 52 and the system returns the response shown, revenue of $5 million. At any point in the process, the user may have the ability to save the ‘report’ or the visualization and the data upon which is relies. That saving of information may also include the metadata above. As mentioned above, the system has the ability to ‘learn’ or make itself more efficient. One way the system may learn is to store the report and its parameters. When the user logs in again, the system could offer that report for the user's review, or could offer to update the information in the report. This gives the system a head start on generating a new report. As the user continues to use the system, the number of previously-accomplished searches and results will be available for the system to use.

In one embodiment, the system learns on several different aspects of interactions with the user. These include, but are not limited to: frequently used terms in the search by a user; frequently used visualization by the user, based on the number of metrics and breakdowns (dimensions); frequently used data values, such as product:iPod, iPad etc., frequently queried metrics; time of the day, week and month a user is searching for certain information, for example, during the close of the quarter what metrics do people in the finance department query; and location where the user queries the data if enabled in a mobile device. The system builds a graph of different nodes, such as User, Terms, Metrics, Time of Day and Location. It then uses a combination of graph traversal algorithms in which all of the nodes are visited in a set sequence, updating and/or checking their data during the sequence, text clustering algorithms in which text is clustered using descriptors and descriptor extraction, and Naive Bayesian algorithms in which probabilistic classifiers based upon Bayes' theorem with strong, or naive, independence assumptions between the features, to learn the user preferences and also relative relevancy of matches.

Referring back to FIG. 3, the user only chose one search term, ‘revenue.’ The user may be given the ability to make this more definite, by clicking next to the already-selected term in the search bar. In FIG. 4, the user adds 2010 as an added parameter, and the response shows revenue for $2 million. In addition in FIG. 4, one can see a drop down list 56 of suggested other parameters. As mentioned above, the search process may document and save the search terms and their associated queries for future use. In one embodiment, they are stored by user, so the system can access a list of terms used by the user and the statistics of the most frequently used terms. Accessing this list would allow the system to populate the drop down list 56.

In FIG. 5, the user has added in the parameter of revenue by product. The system automatically generates a new view, in this case a pie chart. The added terms may be typed in by the user or may be accessed by a pulldown list 56 as shown in FIG. 4. These options also include the user typing in the questions discussed above, like “Compare 2011 Q1 revenue to 2012 Q3.”

As the system presents visualizations such as the pie chart in FIG. 5, it can also offer other formats for the user. In some cases, the user may type in the terms ‘pie chart’ or ‘bar graph’ in the search bar. The system would then present the appropriate visualization. The system can use this as another opportunity to learn about the user and gather statistics as to what types of visualizations the user prefers. This particular user may prefer pie charts, so the system may default to presenting information to the user in pie charts. The level of complexity of the information is left up to the system designer as to how much information about the user the system should use and to what level of granularity.

For example, in FIG. 6, the additional element of ‘target revenue’ is added. There are now four elements to the user's search, each of which will have its own query statements, results and conversion to visualization. The user may have different visualization depending upon the number of elements of the search, or the nature of the search. He or she may prefer comparative bar charts as shown in FIG. 6 when there is a ‘target’ or ‘projected’ or the term ‘versus’ in the original search request. The system gathers all of this information and accesses it when the user logs in and performs a search. The text-to-query generator can retrieve it from the BI server and use it to generate the visualizations. Also shown in FIG. 6 is a side bar 62 that displays other visualization options to the user. If the user starts selecting one of the other charts more frequently, the system will gather this statistic as well and use it to learn the user's preferences.

FIG. 7 shows an alternative view for presentation to the user. One should note that the sidebar only shows this type of chart and pie charts as options. This may be based upon the user's preferences as noted above, or may be based upon the level of complexity of the query. However, this does not limit the user to those particular types. Under the term ‘chart’ in the search bar, as shown in FIG. 8, the pull down list allows the user to select one of many different charts. As the user uses the system and this information is gathered, the system will start to be more accurate in matching the offered visualizations with the user's preference, making the system more efficient and user friendly.

FIG. 9 shows the resulting line bar chart, with other line bar charts offered to the right. If the user decides that he or she does not want to see the discount amount that is included with the chart, highlighted in FIG. 9, the user can remove it. Each of the search terms in the search bar will have their own pull down list to allow the user to change or delete that term. In FIG. 10 shows an example in which the user as removed the discount amount term from the search bar. The removal of a term is not necessarily depending upon the other terms in the search bar.

One should note that the above user interfaces were shown in ‘desktop’ form, but will have mobile versions as well. Using this system, a relatively unsophisticated user can access the wealth of data stored in their organizations business intelligence system. The system can gather statistics on the user's interaction with the system to allow it to offer suggestions as to search terms, visualizations, reports, etc. The storage of the resulting searches and visualizations will also allow the system to have access to already formatted query statements and other information to make it run more efficiently.

It will be appreciated that several of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. Also that various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the embodiments here. 

What is claimed is:
 1. A computer-implemented method of executing a user query, comprising: presenting a user interface to allow a user to enter a query; receiving a user-entered textual request through the interface; launching a search service to rewrite the textual request into a search query; sending the search query to a presentation server; receiving an answer to the query; and returning the answer to the user as a graphical representation.
 2. The computer-implemented method of claim 1, wherein presenting a user interface to the user includes presenting a user interface having options from entry of further search parameter.
 3. The computer-implemented method of claim 1, further comprising converting text from the index searchers to query prior to sending the search query to a presentation server.
 4. The computer-implemented method of claim 1, wherein the user interface is presented on a mobile device.
 5. The computer-implemented method of claim 1, further comprising creating an index searcher based upon the search query.
 6. The computer-implemented method of claim 1, further comprising receiving user inputs related to the answer.
 7. The computer-implemented method of claim 7, further comprising starting a new query in response to the user inputs.
 8. The computer-implemented method of claim 1, further comprising generating usage-based statistical models to find relevant content for the query.
 9. The computer-implemented method of claim 8, further comprising storing the query and the relevant content for that query for later retrieval.
 10. The computer-implemented method of claim 1, further comprising learning user preferences for at least one of searching and visualizations of the data.
 11. The computer-implemented method of claim 10, wherein learning user preferences comprises building a graph having at least one node corresponding to the user, and at least one other node.
 12. The computer-implemented method of claim 10, further comprising applying graph traversal, text clustering and naive Bayesian process to the graph.
 13. The computer-implemented method of claim 10, wherein the at least one other node comprises at least one of terms, metrics, time of day, and location.
 14. The computer-implemented method of claim 10, wherein learning user preferences for visualization comprises tracking frequently used visualizations by the user.
 15. A computer-implemented method, comprising: receiving a crawl request; launching a crawl manager to monitor the crawl request and track statistics related to the crawl; starting a crawl task based upon the crawl request; indexing a business intelligence presentation server to create a data index; and storing the data index.
 16. The computer-implemented method of claim 15, wherein receiving a crawl request comprises receiving a crawl request from a user.
 17. The computer-implemented method of claim 16, further comprising notifying the user that the crawl has started.
 18. The computer-implemented method of claim 17, wherein notifying the user that the crawl has started further comprises providing the user with the ability to stop the crawl.
 19. The computer-implemented method of claim 15, further comprising notifying a user when the crawl is completed.
 20. The computer-implemented method of claim 15, wherein receiving the crawl request comprises receiving a crawl request from a crawl manager based upon a regular crawl schedule. 